This description relates generally to precision agriculture, and more specifically to techniques for generating virtual data models of plants captured by images.
Identifying plants from captured images is beneficial for a number of agricultural purposes. However, individual plants are generally planted close in proximity to each other in order to maximize a desired outcome (e.g., maximize yield, protein percentage, or some other measurable quantity) while minimizing the amount of land that is needed to grow the crops. Based on this, it is common for the leaves, branches, and other growths of a plant to overlap with other nearby plants. As these growths are usually both numerous and roughly similar in appearance from plant to plant, existing image recognition systems experience difficulty when trying to identify plant matter than may appear to belong to multiple nearly overlapping plants. Often, they will mischaracterize plant growths as belonging to the wrong plant, or will misidentify how many plants are present in the field.